Identification of time-varying system and energy-based optimization of adaptive control in seismically excited structure

The combination of structural health monitoring and vibration control is of great importance to provide components of smart structures.While synthetic algorithms have been proposed,adaptive control that is compatible with changing conditions still needs to be used,and time-varying systems are required to be simultaneously estimated with the application of adaptive control.In this research,the identification of structural time-varying dynamic characteristics and optimized simple adaptive control are integrated.First,reduced variations of physical parameters are estimated online using the multiple forgetting factor recursive least squares(MFRLS)method.Then,the energy from the structural vibration is simultaneously specified to optimize the control force with the identified parameters to be operational.Optimization is also performed based on the probability density function of the energy under the seismic excitation at any time.Finally,the optimal control force is obtained by the simple adaptive control(SAC)algorithm and energy coefficient.A numerical example and benchmark structure are employed to investigate the efficiency of the proposed approach.The simulation results revealed the effectiveness of the integrated online identification and optimal adaptive control in systems.

Efficient and robust missing key tag identification for large-scale RFID systems

Radio Frequency Identification(RFID)technology has been widely used to identify missing items.In many applications,rapidly pinpointing key tags that are attached to favorable or valuable items is critical.To realize this goal,interference from ordinary tags should be avoided,while key tags should be efficiently verified.Despite many previous studies,how to rapidly and dynamically filter out ordinary tags when the ratio of ordinary tags changes has not been addressed.Moreover,how to efficiently verify missing key tags in groups rather than one by one has not been explored,especially with varying missing rates.In this paper,we propose an Efficient and Robust missing Key tag Identification(ERKI)protocol that consists of a filtering mechanism and a verification mechanism.Specifically,the filtering mechanism adopts the Bloom filter to quickly filter out ordinary tags and uses the labeling vector to optimize the Bloom filter's performance when the key tag ratio is high.Furthermore,the verification mechanism can dynamically verify key tags according to the missing rates,in which an appropriate number of key tags is mapped to a slot and verified at once.Moreover,we theoretically analyze the parameters of the ERKI protocol to minimize its execution time.Extensive numerical results show that ERKI can accelerate the execution time by more than 2.14compared with state-of-the-art solutions.

木托盘RFID标签安装方式有限元分析及试验研究

目的采用仿真和试验手段并用的方法,研究RFID标签在木质平托盘上的适用安装方式及其对使用性能的影响。方法进行Ansys有限元仿真,分析木质平托盘承载和冲击试验中采用螺钉紧固及黏结剂粘贴方式安装的RFID标签所受应力。作为对比,在实验室进行实物试验,测量RFID标签表面应力,并测试标签读写性能。结果在托盘承载和冲击试验中,从RFID标签各构件及标签连接件的受力分析看,2种安装方式下的RFID标签均未出现脱落和物理性损坏。冲击试验后的RFID标签能够通过读写功能测试。有限元仿真与实物试验的应力分析结果相互吻合,验证了结果的正确性。结论RFID标签采用螺钉紧固和黏结剂粘贴的安装方式都能满足使用要求,且螺钉连接的耐用性更好。研究结果为RFID标签在木质平托盘的安装方式选择提供了理论依据。

基于RFID的医疗设备远程管理系统设计

为提高医院内设备定位精度,设计基于RFID的医疗设备远程管理系统。通过阅读器、电子标签和处理器组成RFID节点,引入AS3992芯片作为核心芯片,实现数据处理和编码,执行远程指令。采用自由空间传播模型计算路径损耗产生值,通过三角定位数学方法计算待定标签位置,采用RFID定位方法确定标签的最近邻居,实现医疗设备的远程定位。实验结果表明,在没有障碍物的情况下,所提系统设备定位误差少于1 m,在有障碍物的情况下,定位误差在2 m左右,证明所提出系统定位准确性较高,满足系统的设计需求。

RFID电缆中间接头无线无源湿度监测

针对电缆中间接头受潮导致的运行故障问题,提出一种基于13.56 MHz RFID技术的无源湿度监测方法。该文利用HFSS建立传感器模型,调整天线匝距使天线达到理想状态,并对天线进行谐振匹配;然后研究传感器天线在水中的传输效果及存在金属屏蔽层时金属对天线性能的影响;最后将所设计的传感器放置于中间接头内部进行性能测试。结果表明:天线匝距为0.6 mm时传感器性能最佳;在天线外部增加47μH电容可实现13.56 MHz谐振匹配;传感器天线在水中的传输效果良好;加装金属屏蔽层可解决路径上金属对传感器信号传输的抑制作用;实际测量距离可达到8.2 cm、相对湿度测量误差最大为0.48%,最大波动幅度仅为0.3%,传感器长期处于高湿度环境仍能准确测量。该方法可实现电缆中间接头内部湿度的直接、快速及无源测量,对防止电缆受潮具有一定的参考意义。

基于RFID和磁导航的AGV小车S7-1200PLC控制系统设计

该项目利用S7-1200PLC作为AGV主控制器,采取驱动轮差速进行转向控制,采用磁导航传感器和RFID标签传感器实现循迹和定位功能,实现AGV小车在立体仓库、VMC850数控加工中心、CK40S数控车床、RB20工业机器人、装配单元之间产品的搬运功能。

基于单天线视轴信号传播模型的RFID定位方法

为了解决现有单天线超高频RFID技术在室内静态目标定位中精度不高的问题,本文提出了一种基于天线视轴信号传播模型的RFID定位新方法。该方法首先通过天线纵向扫描确定目标的高度位置;其次,调整天线高度与目标高度一致,然后步进旋转扫描以识别目标的方向角;进一步的,利用麻雀搜索算法优化的反向传播神经网络建立路径损耗模型并用于测距;最后,综合高度、方向角和距离数据完成目标的定位。实验结果表明,在室内环境测试中,所提出的方法平均定位误差为7.2 cm,能够满足一般室内场景下物品的定位需求。

基于RFID系统的智能图书馆图书盘点探究

智能图书馆时代图书盘点工作依然是图书馆一项重要的基础性工作。文章对基于RFID系统的智能图书馆图书盘点问题进行探究,并分析问题产生的原因,提出了相应的解决对策,以提高图书盘点工作质量和读者自助借还满意度。

基于RFID的智能分类编目系统设计与实现

阐述基于RFID技术的智能分类编目系统设计,它可有效提升图书馆对图书进行分类编目的效率、降低管理成本。RFID技术的应用,有效避免人为原因造成的分类错误。

基于UWB和RFID的装配式建筑堆场管理系统研究

针对装配式混凝土建筑构件种类繁杂、尺寸较大的特点,在大规模堆场中采用人工或二维码难以有效识别和定位构件,文章基于UWB(超宽带)和RFID(射频识别)技术构建了分为四个层级的堆场管理系统,进而将功能模块划分为识别模块、定位模块、监控模块、数据模块和应用模块,实现对堆场构件的快速识别、精准定位和实时监测,可有效提高堆场利用率、管理效率和质量安全保障程度,促进智能建造技术在装配式建筑实施过程中的应用。

基于RFID技术的风险规避型双渠道供应链投资决策研究

本文在单一零售商和制造商构成的双渠道供应链中仅零售渠道发生库存损耗,研究实施RFID技术消除库存损耗对定价及收益的影响,同时引入均值–方差理论研究零售商的风险厌恶偏好对实施RFID技术的决策影响。研究发现:1) 不采用RFID技术时零售商的库存损耗率、采用RFID技术后的单位标签成本均会影响零售价和批发价,且不影响直销价,进而影响各成员的期望收益;2) 不论是否采用RFID技术,零售商的风险厌恶程度均会影响零售价和批发价,且应用RFID技术后,零售价低于不采用时的零售价,批发价高于不采用时的批发价;3) 当零售商的风险厌恶程度给定时,当且仅当单位标签成本低于某一阈值,零售商和制造商均可从RFID技术中获益,且这个阈值与产品生产成本、零售商的库存损耗率有关。

基于RFID技术的高校管制化学品管理

高校作为教育实践和科学研究的重要场所,通常需要使用到种类繁多、性质复杂、易燃易挥发的管制类药品。管制类药品的有效管理是高校实验室安全稳定的重要因素之一。基于RFID技术在高校管制化学品管理上的应用,实行“一瓶一码”,对每瓶化学品进行了精准管理,精确掌握管制化学品从“申购”到“消瓶”的完整流向,实现了全流程闭环管理,极大提升管制化学品的管理效率。

Systematic Evaluation of Pharmacognostic Identification of Polygonum capitatum

[Objectives] To investigate the systematic evaluation of pharmacognostic identification of Polygonum capitatum . [Methods] 10 batches of P. capitatum cultivated in Guizhou were chosen for plant samples. Macroscopical identification was conducted on plant roots, stems, leaves, flowers and fruits. The P. capitatum powder was processed for physical and chemical distinction by FeCl 3 chromogenic reaction, hydrochloric acid magnesium powder reaction, AlCl 3 color development reaction and thin-layer chromatography.Microscope identification was carried out on the powder. Plant genome DNeasy Plant Kit was adopted for DNA molecular marker identification. [Results] The results showed that the stem of P. capitatum was tufted, the leaves were oval, 2 to 5 cm long, and 1 to 2 cm wide;the leaf apex was acute and cuneate at the base, the inflorescence was capitate, paired or solitary;the raceme was erect and nearly spherical, and the perianth was light red. Furthermore, for the chromogenic reaction of FeCl 3 ethanol extract of P. capitatum , appeared blue and turned to dark blue after long time storing at room temperature. For the reaction of hydrochloric acid magnesium powder, the alcohol extract of P. capitatum , exhibited deep red. In the color reaction of AlCl 3, the alcohol extract revealed yellow fluorescence under 360 nm UV lamp. Microscope identification of the powder displayed pollen grains, crystal sheath fibers, cellulose, vessels, starch grains, cork cells, and other characteristic fragments. In addition, DNA barcoding electrophoresis results showed that P. capitatum showed a clear and bright single band near 500 bp, and further sequencing results showed that the sequence differences were mainly concentrated in ITS1 and ITS2 region. [Conclusions] Systematic evaluation for the identification of P. capitatum is established, which combines with macroscopic identification, physicochemical identification, powder microscope identification, and DNA molecular identification. Finally, the original medicinal material is identified as P. capitatum Buch.-Ham. ex D. Don.

Whale Optimization Algorithm-Based Deep Learning Model for Driver Identification in Intelligent Transport Systems

Driver identification in intelligent transport systems has immense demand,considering the safety and convenience of traveling in a vehicle.The rapid growth of driver assistance systems(DAS)and driver identification system propels the need for understanding the root causes of automobile accidents.Also,in the case of insurance,it is necessary to track the number of drivers who commonly drive a car in terms of insurance pricing.It is observed that drivers with frequent records of paying“fines”are compelled to pay higher insurance payments than drivers without any penalty records.Thus driver identification act as an important information source for the intelligent transport system.This study focuses on a similar objective to implement a machine learning-based approach for driver identification.Raw data is collected from in-vehicle sensors using the controller area network(CAN)and then converted to binary form using a one-hot encoding technique.Then,the transformed data is dimensionally reduced using the Principal Component Analysis(PCA)technique,and further optimal parameters from the dataset are selected using Whale Optimization Algorithm(WOA).The most relevant features are selected and then fed into a Convolutional Neural Network(CNN)model.The proposed model is evaluated against four different use cases of driver behavior.The results show that the best prediction accuracy is achieved in the case of drivers without glasses.The proposed model yielded optimal accuracy when evaluated against the K-Nearest Neighbors(KNN)and Support Vector Machines(SVM)models with and without using dimensionality reduction approaches.

A systematic machine learning method for reservoir identification and production prediction

Reservoir identification and production prediction are two of the most important tasks in petroleum exploration and development.Machine learning(ML)methods are used for petroleum-related studies,but have not been applied to reservoir identification and production prediction based on reservoir identification.Production forecasting studies are typically based on overall reservoir thickness and lack accuracy when reservoirs contain a water or dry layer without oil production.In this paper,a systematic ML method was developed using classification models for reservoir identification,and regression models for production prediction.The production models are based on the reservoir identification results.To realize the reservoir identification,seven optimized ML methods were used:four typical single ML methods and three ensemble ML methods.These methods classify the reservoir into five types of layers:water,dry and three levels of oil(I oil layer,II oil layer,III oil layer).The validation and test results of these seven optimized ML methods suggest the three ensemble methods perform better than the four single ML methods in reservoir identification.The XGBoost produced the model with the highest accuracy;up to 99%.The effective thickness of I and II oil layers determined during the reservoir identification was fed into the models for predicting production.Effective thickness considers the distribution of the water and the oil resulting in a more reasonable production prediction compared to predictions based on the overall reservoir thickness.To validate the superiority of the ML methods,reference models using overall reservoir thickness were built for comparison.The models based on effective thickness outperformed the reference models in every evaluation metric.The prediction accuracy of the ML models using effective thickness were 10%higher than that of reference model.Without the personal error or data distortion existing in traditional methods,this novel system realizes rapid analysis of data while reducing the time required to resolve reservoir classification and production prediction challenges.The ML models using the effective thickness obtained from reservoir identification were more accurate when predicting oil production compared to previous studies which use overall reservoir thickness.

Structural Damage Identification System Suitable for Old Arch Bridge in Rural Regions: Random Forest Approach

A huge number of old arch bridges located in rural regions are at the peak of maintenance.The health monitoring technology of the long-span bridge is hardly applicable to the small-span bridge,owing to the absence of technical resources and sufficient funds in rural regions.There is an urgent need for an economical,fast,and accurate damage identification solution.The authors proposed a damage identification system of an old arch bridge implemented with amachine learning algorithm,which took the vehicle-induced response as the excitation.A damage index was defined based on wavelet packet theory,and a machine learning sample database collecting the denoised response was constructed.Through comparing three machine learning algorithms:Back-Propagation Neural Network(BPNN),Support Vector Machine(SVM),and Random Forest(R.F.),the R.F.damage identification model were found to have a better recognition ability.Finally,the Particle Swarm Optimization(PSO)algorithm was used to optimize the number of subtrees and split features of the R.F.model.The PSO optimized R.F.model was capable of the identification of different damage levels of old arch bridges with sensitive damage index.The proposed framework is practical and promising for the old bridge’s structural damage identification in rural regions.

集成双频无源RFID传感器标签设计

针对超高频和高频RFID传感器由于各自测量距离和使用便捷性而导致应用场合的局限,提出一种集成超高频和高频的双频无源RFID传感器标签设计方法。首先,集成超高频和高频射频供电和数据收发模块,达到超高频和高频双频读写目的;其次,为提高双频RFID传感器标签在超高频模式下的读取距离,采用电荷泵对射频收发芯片输出电压进行升压管理以延长放电时间;然后,为增强传感器标签的通用性,将测量数据集成到高频NDEF消息和超高频用户数据段中。根据以上方法,设计了双频无源RFID温湿度传感器标签。同时搭建了实验平台对电荷泵提升读取距离的可行性和传感器标签功能进行了实验验证。实验结果表明:该传感器标签可以同时支持超高频和高频2种读取模式;使用电荷泵升压能量管理有效提升了读取距离;可通过支持EPCC1G2协议的通用超高频读写器和NFC手机方便地获取传感器测量数据。

Research on strategic risk identification method of equipment system development based on system dynamics

Strategic management of equipment system develop-ment must attach importance to effective strategic risk manage-ment.Aiming at the identification of strategic risk of equipment system development,firstly,the source of strategic risk of equip-ment system development is analyzed and classified.Based on this,a causal loop diagram of strategic risk of equipment sys-tem development based on system dynamics is established.The system dynamics analysis software Vensim PLE is used to carry out the risk influencing factors analysis,risk consequences ana-lysis,risk feedback loop identification and corresponding pre-control measures,and achieves a good risk identification effect.

基于RFID的仓储自动化与智能化技术应用

随着全球供应链的不断发展及复杂化,仓储自动化与智能化技术成为提高效率及准确性的关键手段。探讨了射频识别(RFID)在仓储管理中的应用,利用RFID技术可实时追踪商品,减少库存错误,降低人工操作需求,提高整体物流速度。将传统的条形码系统与RFID系统进行对比,揭示RFID在准确性、可靠性及数据收集速度等方面的优势,为未来的仓储自动化与智能化发展提供参考。

基于RFID技术的医疗器械信息化追溯系统

为了增强医疗器械系统的整体追溯性能,设计了基于RFID技术的医疗器械信息化追溯系统。在医疗器械信息化追溯系统整体架构下,设计系统硬件部分,包括读写器管理模块、基于RFID技术的数据采集及传输模块、追溯数据处理模块、追溯数据管理模块等,通过上述模块完成对医疗器械信息追溯数据的采集、传输、处理、调用及管理;系统的软件部分,在生产、流通、责任等3个追溯环节下,通过LANDMARC算法和VIRE算法对医疗器械信息追溯,获取追溯标签的最终位置坐标。通过系统CPU内存占用率测试、存储时间测试和追溯效率测试验证了该系统的有效性。